Computational Fluid Dynamics
Theory and Python Implementations
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Beschreibung
This textbook provides unified treatment of computational fluid dynamics, systematically developing theory, algorithms, and applications from first principles with complete Python implementations. It addresses the critical pedagogical gap in CFD education: the absence of a modern textbook that integrates mathematical rigor with accessible, reproducible computational implementations in Python, the dominant language for scientific computing education. The book covers essential numerical methods (finite difference, finite volume, finite elements, spectral) for both compressible and incompressible flows, treating canonical problems (diffusion, advection, Burgers equation) as pedagogical foundations building toward realistic applications. Modern topics including machine learning for CFD and adaptive mesh refinement are positioned as natural extensions of classical methods. Comprehensive benchmark problems (lid-driven cavity, flow past cylinder, shock tubes, Taylor-Green vortex) provide validated reference solutions and project templates.
- Includes complete production-quality Python implementations for all methods enabling immediate experimentation
- Integrates machine learning and adaptive mesh refinement as natural extensions of classical CFD methods
- Follows a systematic progression from canonical 1D problems through realistic 3D applications with benchmarks
This textbook provides unified treatment of computational fluid dynamics, systematically developing theory, algorithms, and applications from first principles with complete Python implementations. It addresses the critical pedagogical gap in CFD education: the absence of a modern textbook that integrates mathematical rigor with accessible, reproducible computational implementations in Python, the dominant language for scientific computing education. The book covers essential numerical methods (finite difference, finite volume, finite elements, spectral) for both compressible and incompressible flows, treating canonical problems (diffusion, advection, Burgers equation) as pedagogical foundations building toward realistic applications. Modern topics including machine learning for CFD and adaptive mesh refinement are positioned as natural extensions of classical methods. Comprehensive benchmark problems (lid-driven cavity, flow past cylinder, shock tubes, Taylor-Green vortex) provide validated reference solutions and project templates.
Includes complete production-quality Python implementations for all methods enabling immediate experimentation Integrates machine learning and adaptive mesh refinement as natural extensions of classical CFD methods Follows a systematic progression from canonical 1D problems through realistic 3D applications with benchmarks
Autor*in
Omer San
Themen in »Computational Fluid Dynamics«
Computational fluid dynamics CFD Python finite volume methods spectral methods incompressible Navier-Stokes compressible flows Riemann solvers shock-capturing schemes projection methods WENO schemes adaptive mesh refinement machine learning CFD physics-informed neural networks turbulence modeling numerical methods